Inspiration
Every day, thousands of liver function tests are taken in the millions of labs in this country. Analyzing these records and searching for patterns is a very tedious task. This solution makes it easier for lab technicians to recognize patterns in data. These patterns help in laying out a path towards further tests which must be done to find out the cause of the abnormality.
What it does
Depending on the cause of the abnormality further tests can be recommended. The 3 major causes of abnormality are cholestatic, hepatocellular damage, and synthetic function. Thus, it dramatically reduces the workload on lab technicians who have to sift through hundreds of liver function tests daily.
Challenges we ran into
We have used a clustering-based approach, and one of the main challenges was to find which clustering algorithm to use. We initially started out with rule-based clustering. But, this classified most of the records as abnormal, which contradicts what was actually advised to us. So we switched to agglomerative hierarchical clustering. But due to the sheer size of the dataset we were given, it failed. A similar problem occurred during the DBSCAN clustering. Thus, after trial and error, we came to k-means algo which gave us the appropriate results and patterns.
What's Next for LFT Pattern Analysis Using Clustering
Developing the project into complete functionality
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